Vast quantities of data are generally available related to performance tracking for transportation fleets and individual assets. For example, the aviation industry gathers aircraft operational data from a variety of particular sources. Data can be collected from aircraft via Quick Access Recorders (QARs), which can provide airborne recordation of raw flight data parameters received from a number of aircraft sensors and avionic systems. Raw flight data parameters can include, for example, location data defining aircraft trajectories as well as other sensed parameters related to aircraft performance and the like.
Predictive analysis of vehicle operational data (e.g., aircraft flight data) can offer useful information for maintenance and prognostics for individual vehicles or entire fleets. This information can benefit engineers, managers, or other specialists within a vehicle maintenance organization who help solve various vehicle and/or fleet maintenance problems. Many existing systems rely primarily on human interpretation of these vast amounts of data, which can be cumbersome, tedious and time consuming. In addition, there are limitations with visualizing fleet data in mass in a manner that accommodates meaningful analysis, such as comparison of aircraft data across multiple flights.